In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation proposal
Segmentation, Analysis and Visualization of Large Microvascular Networks in the Rat Brain
Advances in high-throughput imaging allow researchers to collect three-dimensional images of whole organ microvascular networks. These networks are highly complex, making them difficult to segment, visualize, analyze, and synthesize. These limitations make it particularly difficult to study tissue microstructure, since microvasculature plays a prominent role in tissue development and disease progression. In addition, tissue engineering requires the generation of similar networks in synthesized tissue samples. The underlying complexity stems from microvascular networks tending to be highly interconnected and heterogeneous. This presents a difficult visualization problem since visual clarity is disrupted by the high density. This structure also hampers analysis, making it difficult to study microvascular changes as a consequence of disease progression. Current research suggests that microvascular networks play a particularly prominent role in neurodegenerative disease. In this proposal, I present a framework for working with micro-vascular networks embedded in multi-terabyte three dimensional images collected using high-throughput microscopy. I propose an efficient and tune-able algorithm for segmentation utilizing graphics hardware, making this framework accessible to researchers relying on limited hardware in standard workstations. I propose multiple visualization methods that provide scientists with the ability to highlight important features in complex microvasculature structures. I propose a graph based method for collecting statistical features from segmented networks, which can be used to classify differences in networks due to tissue type or disease progression. Finally, these statistics will be used to create synthetic networks that are structurally similar to realistic microvasculature and viable for manufacture in engineered tissue models.
Date: Monday, August 20, 2018
Time: 10:00 AM
Place: PGH 550
Advisors: Dr. Guoning Chen
Faculty, students, and the general public are invited.